A Learning Theoretic Approach to Energy Harvesting Communication System Optimization

TL;DR

This paper introduces a learning-based optimization framework for energy harvesting communication systems, maximizing data transmission without prior knowledge of the stochastic processes involved, and compares different information scenarios.

Contribution

It presents a novel learning theoretic approach for optimizing energy harvesting communications without assuming prior process knowledge.

Findings

Performance loss due to lack of process information quantified

Optimal solutions compared across different information availability scenarios

Online and offline optimization frameworks developed

Abstract

A point-to-point wireless communication system in which the transmitter is equipped with an energy harvesting device and a rechargeable battery, is studied. Both the energy and the data arrivals at the transmitter are modeled as Markov processes. Delay-limited communication is considered assuming that the underlying channel is block fading with memory, and the instantaneous channel state information is available at both the transmitter and the receiver. The expected total transmitted data during the transmitter's activation time is maximized under three different sets of assumptions regarding the information available at the transmitter about the underlying stochastic processes. A learning theoretic approach is introduced, which does not assume any a priori information on the Markov processes governing the communication system. In addition, online and offline optimization problems are studied for the same setting. Full statistical knowledge and causal information on the realizations of the underlying stochastic processes are assumed in the online optimization problem, while the offline optimization problem assumes non-causal knowledge of the realizations in advance. Comparing the optimal solutions in all three frameworks, the performance loss due to the lack of the transmitter's information regarding the behaviors of the underlying Markov processes is quantified.